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Rainfall prediction method and system based on wavelet decomposition

A technology of wavelet decomposition and forecasting method, which is applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of less research, and achieve the effects of high accuracy, strong promotion, and wide versatility in watersheds

Pending Publication Date: 2021-01-19
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above studies are mainly aimed at predicting daily or monthly rainfall data, and the ongoing rainfall data are less real-time measurement data and most of them are non-stationary data, so there are few related studies.

Method used

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  • Rainfall prediction method and system based on wavelet decomposition
  • Rainfall prediction method and system based on wavelet decomposition
  • Rainfall prediction method and system based on wavelet decomposition

Examples

Experimental program
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Embodiment 1

[0044] This embodiment provides a rainfall prediction method based on wavelet decomposition;

[0045] Rainfall prediction method based on wavelet decomposition, including:

[0046] S101: Obtain several historical rainfall time series and the current ongoing rainfall time series;

[0047] S102: From several historical rainfall time series, filter out several historical rainfall time series with correlation with the current ongoing rainfall time series greater than the set threshold;

[0048] S103: Decompose several historical rainfall time series and current rainfall time series whose correlation degree is greater than the set threshold, and obtain low frequency components (d1-d5) and high frequency components (a5) respectively;

[0049] S104: building a VAR model for the low-frequency component to predict, and obtaining the low-frequency component prediction; building a NAR neural network for the high-frequency component to predict, and obtaining the high-frequency component ...

Embodiment 1

[0101] The Tuhai River is located in the south of the Haihe River Basin in Shandong Province. It flows through the four cities of Liaocheng, Dezhou, Jinan and Binzhou in turn, and flows into the Bohai Bay at Dongfenggang Storm Station, Zhanhua County, Binzhou City. The drainage area is 13,296km 2 . The annual average rainfall in the Tuhai River Basin is 616.62mm, and the main source of runoff is rainfall, which is mainly concentrated in July-August. During the flood season, flood disasters occur frequently, and droughts and floods or consecutive droughts and floods often occur. The threat to the safety of people's life and property in the basin is relatively large, which seriously restricts the economic development of the basin. Therefore, this example selects the historical rainfall data of 9 fields and the real-time data of the 10th field of ongoing rainfall at Yanggu Station in the Tuhai River Basin as an example to predict the amount of ongoing rainfall.

[0102] The Raida ...

Embodiment 2

[0117] The Tuhai River is located in the south of the Haihe River Basin in Shandong Province. It flows through the four cities of Liaocheng, Dezhou, Jinan and Binzhou in turn, and flows into the Bohai Bay at Dongfenggang Storm Station, Zhanhua County, Binzhou City. The drainage area is 13,296km 2 . The annual average rainfall in the Tuhai River Basin is 616.62mm, and the main source of runoff is rainfall, which is mainly concentrated in July-August. During the flood season, flood disasters occur frequently, and droughts and floods or consecutive droughts and floods often occur. The threat to the safety of people's life and property in the basin is relatively large, which seriously restricts the economic development of the basin. Therefore, this example selects the historical rainfall data of 6 events and the real-time data of the 7th field of ongoing rainfall at Dong'a Station in the Tuhai River Basin as an example, and forecasts the amount of ongoing rainfall.

[0118] The R...

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Abstract

The invention discloses a rainfall prediction method and system based on wavelet decomposition. The rainfall prediction method comprises the following steps: acquiring a plurality of historical rainfall time sequences and a current rainfall time sequence; screening out the historical rainfall time sequences from the plurality of historical rainfall time sequences, wherein the relevancy between thehistorical rainfall time sequences and the currently-performed rainfall time sequence is greater than a set threshold value; decomposing the plurality of historical rainfall time sequences of which the relevancy is greater than a set threshold value and the currently performed rainfall time sequence to obtain a low-frequency component and a high-frequency component respectively; constructing a VAR model for the low-frequency component for prediction to obtain a low-frequency component prediction quantity; constructing an NAR neural network for the high-frequency component for prediction to obtain a high-frequency component prediction quantity; and reconstructing the low-frequency component predictor and the high-frequency component predictor to obtain a rainfall predictor.

Description

technical field [0001] This application relates to the technical field of rainfall prediction, in particular to a method and system for rainfall prediction based on wavelet decomposition. Background technique [0002] The statements in this section merely mention the background technology relevant to the present application, and do not necessarily constitute prior art. [0003] Floods are one of the most frequent natural disasters in my country. Accurate prediction of rainfall data can provide scientific decision-making suggestions for flood control work, thereby improving the quality of life of residents and promoting regional political and economic development. [0004] Scholars at home and abroad have conducted relevant research on rainfall prediction: Chen Xiaoping took the radar echo intensity data of Zhejiang Province from January to October 2016 as an example to predict rainfall, and constructed a random forest model, a BP neural network model and a convolutional neur...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06F30/27G06F17/18G06F16/2458G06F16/215
CPCG06Q10/04G06F30/27G06F17/18G06F16/2474G06F16/215Y02A90/10
Inventor 赵然杭甘甜张瑜轩王好芳逄晓腾刘子莹李智齐真周璐张晴晴李华兴蒋殿顺刘珺
Owner SHANDONG UNIV
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